We performed a simplified version of sci-RNA-seq3, further optimized for ‘tiny’ samples20 . In brief, to each tube, 100 μl of a hypotonic, PBS-based lysis buffer was added with DEPC as an RNase inhibitor. The resulting nuclei were then fixed with four volumes of a mix of methanol and dithiobis (succinimidyl propionate) (DSP). After rehydrating and washing the nuclei carefully in a sucrose/PBS/triton/MgCl2 buffer (SPBSTM), the nuclei were distributed to two 96-well plates for reverse transcription, allocating 8 wells per embryo. After reverse transcription, nuclei were pooled, washed in SPBSTM and redistributed to a fresh plate for ligation of the second index primer with T4 DNA ligase. Nuclei were then again pooled, washed, and redistributed to three final plates for second-strand synthesis, extraction, tagmentation, and PCR to add the third index plus a plate index. Products were pooled by PCR plate, size-selected and sequenced on two Illumina NextSeq runs (NextSeq-1 and NextSeq-2). Samples analysed: n = 8 natural embryos ranging from E7.5 to E9.5, n = 3 for ETiX6, n = 2 for failed ETiX6, 5 for ETiX8, 4 failed ETiX8 and 2 Pax6-knockout ETiX8.
Nextseq run
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The NextSeq is a high-throughput sequencing system designed for a wide range of applications, including gene expression analysis, epigenetics, and small RNA studies. It utilizes a sequencing-by-synthesis approach to generate high-quality sequencing data.
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Lab products found in correlation
6 protocols using nextseq run
1
Optimized Sci-RNA-seq3 for Tiny Samples
2
SARS-CoV-2 Consensus Sequence Generation
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Additional samples, not reported in this study, were included on Illumina NextSeq runs. The raw reads were demultiplexed using bcl2fastq (v2.20) (Illumina Inc.) to produce 311 FASTQ files for the run with the routine samples (112 SARS-CoV-2 samples and 3 negative controls) and the run with the rapid response samples (247 SARS-CoV-2 samples, 4 negative controls, and 2 positive controls) with only the relevant samples analysed in this paper. The reads were used to generate a consensus sequence for each sample using an adapted open source pipeline [15 ]. Briefly, the reads had adapters trimmed with TrimGalore [16 ] and were aligned to the Wuhan-Hu-1 reference genome (accession MN908947.3) using BWA-MEM (v0.7.17) [17 ]; the ARTIC amplicons were trimmed and a consensus built using iVAR (v.1.2.3) [18 (link)].
3
Optimized Sci-RNA-seq3 for Tiny Samples
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
We performed a simplified version of sci-RNA-seq3, further optimized for ‘tiny’ samples20 . In brief, to each tube, 100 μl of a hypotonic, PBS-based lysis buffer was added with DEPC as an RNase inhibitor. The resulting nuclei were then fixed with four volumes of a mix of methanol and dithiobis (succinimidyl propionate) (DSP). After rehydrating and washing the nuclei carefully in a sucrose/PBS/triton/MgCl2 buffer (SPBSTM), the nuclei were distributed to two 96-well plates for reverse transcription, allocating 8 wells per embryo. After reverse transcription, nuclei were pooled, washed in SPBSTM and redistributed to a fresh plate for ligation of the second index primer with T4 DNA ligase. Nuclei were then again pooled, washed, and redistributed to three final plates for second-strand synthesis, extraction, tagmentation, and PCR to add the third index plus a plate index. Products were pooled by PCR plate, size-selected and sequenced on two Illumina NextSeq runs (NextSeq-1 and NextSeq-2). Samples analysed: n = 8 natural embryos ranging from E7.5 to E9.5, n = 3 for ETiX6, n = 2 for failed ETiX6, 5 for ETiX8, 4 failed ETiX8 and 2 Pax6-knockout ETiX8.
4
SARS-CoV-2 Genomic Consensus Sequencing
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Additional samples, not reported in this study, were included on Illumina NextSeq runs. The raw reads were demultiplexed using bcl2fastq (v2.20) (Illumina Inc.) to produce 311 FASTQ files for the run with the routine samples (112 SARS-CoV-2 samples and 3 negative controls) and the run with the rapid response samples (247 SARS-CoV-2 samples, 4 negative controls, and 2 positive controls) with only the relevant samples analysed in this paper. The reads were used to generate a consensus sequence for each sample using an adapted open source pipeline [15] . Briefly, the reads had adapters trimmed with TrimGalore [16] and were aligned to the Wuhan-Hu-1 reference genome (accession MN908947.3) using BWA-MEM (v0.7.17) [17] ; the ARTIC amplicons were trimmed and a consensus built using iVAR (v.1.2.3) [18] .
5
Unidirectional mRNA-Seq Library Preparation
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The preparation of mRNA-Seq libraries and their sequencing has been carried out at the EMBL Genomics Core Facilities, Germany. Preparation of barcoded stranded mRNA-Seq libraries was performed with unidirectional deep sequencing of pooled libraries, read-length 80 bases, Illumina NextSeq run (yield ∼550 million reads/lane), the pool of 15 libraries in one run.
6
Soil Microbiome Profiling via Shotgun Sequencing
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One soil profile from each CZO was selected for shotgun sequencing; we chose the profile that exhibited the most dissimilarity in microbial community composition through depth to sequence. The Eel River CZO samples were not collected in time to be included in these analyses. Using the same DNA as used for the amplicon sequencing, we generated metagenomic libraries with the TruSeq DNA LT library preparation kit (Illumina, San Diego, CA). All samples were pooled and sequenced on an Illumina NextSeq run using 2 × 150-bp paired-end chemistry at the University of Colorado next-generation sequencing facility. Prior to downstream analysis, we merged and quality filtered the paired-end metagenomic reads with USEARCH. After quality filtering, we had an average of 8.8 million quality-filtered reads per sample (range, 1.9 to 15.4 million reads; we included only samples with at last 1 million reads). These sequences were uploaded to MG-RAST (65 (link)) for public access. We used Metaxa2 (66 (link)) with default settings to extract small-subunit (SSU) rRNA gene sequences (bacterial, archaeal, and eukaryotic) in each sample and assigned taxonomy as described above using the Greengenes 13_8 database (60 (link)) and the RDP classifier (61 (link)).
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